Federated learning is a distributed machine learning technique that allows participants to train models locally and upload updates to a central server. The central server aggregates the updates to generate a better global model, ensuring data privacy and solving the problem of data silos. However, the gradient aggregation relies on a central server, which may lead to a single point of failure, and the central server is also a potential malicious attacker. Therefore, federated learning needs to be decentralized. The existing decentralized solutions ignore external adversaries and the performance bottlenecks issues caused by data communication. To address the above issues, this study proposes a decentralized federated learning method considering external adversaries. The method applies Shamir’s secret sharing scheme to divide model updates into multiple shares to protect gradient privacy. The method proposes a flooding consensus protocol that randomly selects a participant as the central server in each round to complete global aggregation, efficiently achieving the decentralization of federated learning. At the same time, the method introduces BLS aggregate signatures to prevent external adversary attacks and improve verification efficiency. Theoretical analysis and experimental results indicate that this method is safe and efficient, having higher efficiency than similar federated learning methods.